School of Fundamental Science and Technology, Graduate School of Keio University, Kanagawa, Japan.
School of Fundamental Science and Technology, Graduate School of Keio University, Kanagawa, Japan; Center of Assistive Robotics and Rehabilitation for Longevity and Good Health, National Center for Geriatrics and Gerontology, Aichi, Japan.
Neuroimage. 2020 Nov 15;222:117249. doi: 10.1016/j.neuroimage.2020.117249. Epub 2020 Aug 14.
A variety of neural substrates are implicated in the initiation, coordination, and stabilization of voluntary movements underpinned by adaptive contraction and relaxation of agonist and antagonist muscles. To achieve such flexible and purposeful control of the human body, brain systems exhibit extensive modulation during the transition from resting state to motor execution and to maintain proper joint impedance. However, the neural structures contributing to such sensorimotor control under unconstrained and naturalistic conditions are not fully characterized. To elucidate which brain regions are implicated in generating and coordinating voluntary movements, we employed a physiologically inspired, two-stage method to decode relaxation and three patterns of contraction in unilateral finger muscles (i.e., extension, flexion, and co-contraction) from high-density scalp electroencephalograms (EEG). The decoder consisted of two parts employed in series. The first discriminated between relaxation and contraction. If the EEG data were discriminated as contraction, the second stage then discriminated among the three contraction patterns. Despite the difficulty in dissociating detailed contraction patterns of muscles within a limb from scalp EEG signals, the decoder performance was higher than chance-level by 2-fold in the four-class classification. Moreover, weighted features in the trained decoders revealed EEG features differentially contributing to decoding performance. During the first stage, consistent with previous reports, weighted features were localized around sensorimotor cortex (SM1) contralateral to the activated fingers, while those during the second stage were localized around ipsilateral SM1. The loci of these weighted features suggested that the coordination of unilateral finger muscles induced different signaling patterns in ipsilateral SM1 contributing to motor control. Weighted EEG features enabled a deeper understanding of human sensorimotor processing as well as of a more naturalistic control of brain-computer interfaces.
各种神经基质参与了自愿运动的启动、协调和稳定，这些运动是由拮抗肌的适应性收缩和松弛支撑的。为了实现对人体的这种灵活和有目的的控制，大脑系统在从休息状态到运动执行的过渡过程中以及在维持适当的关节阻抗时表现出广泛的调节。然而，在不受限制和自然的条件下，有助于这种感觉运动控制的神经结构尚未完全描述。为了阐明哪些大脑区域参与产生和协调自愿运动，我们采用了一种受生理启发的两阶段方法，从高密度头皮脑电图（EEG）中解码单侧手指肌肉的松弛和三种收缩模式（即伸展、弯曲和共同收缩）。解码器由两个串联使用的部分组成。第一部分区分松弛和收缩。如果 EEG 数据被判别为收缩，则第二阶段然后判别三种收缩模式。尽管从头皮 EEG 信号中分离肢体内部肌肉的详细收缩模式具有挑战性，但四分类中的解码器性能比随机水平高出两倍。此外，在训练解码器中的加权特征揭示了 EEG 特征对解码性能的不同贡献。在第一阶段，与先前的报告一致，加权特征定位于与激活手指相对的感觉运动皮层（SM1）周围，而在第二阶段，加权特征定位于同侧 SM1 周围。这些加权特征的位置表明，单侧手指肌肉的协调在同侧 SM1 中引起了不同的信号模式，有助于运动控制。加权 EEG 特征使我们能够更深入地了解人类感觉运动处理，以及对脑机接口的更自然的控制。
